Abstract: Background: Periodontitis is a chronic inflammatory disease increasingly associated with systemic immune dysregulation and microbial imbalance beyond the oral cavity. Emerging evidence suggests that gut microbiome dysbiosis contributes to periodontal inflammation through the oral–gut microbial axis. Methods: This systematic review was conducted according to PRISMA guidelines using the PECOS framework. A comprehensive literature search was performed across PubMed, Embase, Scopus, Web of Science, Cochrane Library, CINAHL, and Google Scholar databases to identify studies evaluating the association between gut microbiota and periodontitis, including microbiome alterations, inflammatory pathways, and microbiome-modulating interventions. Results: The included studies demonstrated that patients with periodontitis frequently exhibit reduced gut microbial diversity, enrichment of pro-inflammatory taxa, impaired intestinal barrier function, and elevated inflammatory mediators including C-reactive protein, interleukin-6, and tumor necrosis factor-alpha. Several studies identified translocation of periodontal pathogens such as Porphyromonas gingivalis and Fusobacterium nucleatum to the gastrointestinal tract, supporting the existence of an oral–gut axis. Probiotics, prebiotics, synbiotics, and periodontal therapy showed potential benefits in improving periodontal parameters and restoring microbial homeostasis. Conclusions: Current evidence supports a significant relationship between gut microbiome dysbiosis and periodontitis through immune, inflammatory, and metabolic mechanisms. However, heterogeneity among studies and limited longitudinal evidence warrant further standardized clinical and mechanistic investigations to establish causality and optimize microbiome-targeted therapeutic strategies in periodontal disease.

Abstract: Sleep disorders affect a substantial proportion of hospitalized patients yet remain among the most systematically underdiagnosed conditions in acute care medicine, with up to 80% of moderate-to-severe cases carrying no formal diagnosis at the time of admission. At the same time, frailty—a state of heightened physiological vulnerability arising from cumulative multi-system biological decline—is present in 40–80% of inpatients and shares deep, bidirectional neurobiological pathways with sleep pathology through shared mechanisms of circadian dysregulation, hypothalamic-pituitary-adrenal axis activation, and chronic low-grade inflammation. Despite this convergence, no study has integrated validated, administratively computable frailty phenotyping with a machine learning framework specifically designed to predict inpatient sleep disorder diagnosis at the point of hospital admission. To address this gap, we developed and evaluated a suite of five binary classification models—XGBoost, Random Forest, LightGBM, CatBoost, and Decision Tree—using 9,682 balanced hospitalization episodes from the MIMIC-IV (version 2.2) database. The predictor set comprised 23 admission-time structured features across three domains: frailty and comorbidity burden, including the Hospital Frailty Risk Score (HFRS) derived from ICD-10 codes, the Elixhauser comorbidity index, prior admission history, and six binary disease flags (obesity, hypertension, type 2 diabetes, heart failure, COPD, and depression/anxiety); physiological and laboratory biomarkers from the first 24 hours of care, including minimum SpO₂, heart rate variability, hemoglobin, creatinine, albumin, and arterial blood gas parameters; and sociodemographic and administrative variables encompassing age, sex, ethnicity, insurance type, and admission acuity. Two binary outcomes were modeled independently: any sleep disorder diagnosis (ICD-10: G47.x) and insomnia specifically (ICD-10: G47.00). Model performance was assessed through five-fold stratified cross-validation and bootstrap confidence intervals (n = 1,000 iterations), with predictor importance quantified using SHapley Additive exPlanations (SHAP). XGBoost achieved the strongest aggregate performance across all evaluation metrics, attaining an area under the receiver operating characteristic curve (AUC) of 0.871 (95% CI: 0.856–0.887), accuracy of 79.6%, F1-score of 0.820, and sensitivity of 94.9%, correctly identifying 903 of 952 true positive cases in the held-out test set; all gradient boosting frameworks substantially outperformed the Decision Tree baseline (AUC 0.836). SHAP analysis identified the HFRS and Elixhauser index as the two dominant predictors, followed by depression/anxiety, obesity, hypertension, and minimum SpO₂—a pattern that is mechanistically consistent with established pathophysiological literature on frailty-associated sleep pathology. The well-calibrated probability outputs of the XGBoost model make it directly suitable for integration into clinical decision support systems, offering a deployable, interpretable screening tool for inpatient sleep disorder identification that requires no dedicated instrumentation beyond routine admission data.

Abstract: The paper seeks to find the direct and indirect association amongst capital structure and firm value among all the nonfinancial firms listed in PSX from (2014-2019). Secondary panel data was used to conduct analysis. Structural equation modelling technique in Stata was used to estimate the direct effects. MedSEM, a special package for Stata, was used to estimate the indirect effects. Results showed that capital structure had no direct effect on value of the firm, but financial distress mediated the association amongst capital structure and value of the firm. Substantial indirect effect clearly manifests the existence of indirect nature of association amongst capital structure and value of the firm.

Abstract: Atmospheric icing is one of the most critical meteorological hazards for unmanned aerial vehicles (UAV), whose operation under adverse conditions—high latitudes, elevated altitudes, long-endurance missions without pilot intervention—particularly exposes them to ice accumulation on aerodynamic surfaces and propellers. Unlike manned aviation, where this phenomenon has been extensively studied and regulated, a significant knowledge gap exists in the UAV domain that limits the development of effective protection systems adapted to energy constraints. This article provides an integrated review of atmospheric ice formation mechanisms, their specific effects on UAV propellers, and the two most promising mitigation approaches: electrothermal modelling for the optimisation of electric heating systems, and the development of functional surface materials, including superhydrophobic coatings (SHC), composites with conductive nanofillers (graphene, carbon nanotubes), and piezoelectric actuators. The analysis demonstrates that hybrid systems combining passive and active strategies managed by intelligent control represent the most viable solution for extending UAV operational envelopes under known icing conditions, with a potential reduction in anti-icing energy consumption exceeding 40% compared to conventional continuous heating. Key research gaps are identified, and a prioritised future research agenda is proposed to support the development of certifiable anti-icing systems for rotary-wing UAV platforms.

Abstract: Zn₂SnO₄ is a promising anode for lithium-ion batteries owing to its high theoretical capacity, yet its pratical utilization is severely limited by sluggish reaction kinetics, large volume expansion, and unstable electrode/electrolyte interfaces. Here, we intro-duce a dimensionality-reduction strategy that simultaneously boosts capacity and cy-cling stability. Through surfactant-directed crystal growth, acid-etching reconstruction, and hydrothermal carbon coating, compact Zn₂SnO₄ octahedra are controllably trans-formed into sheet-assembled structures and finally into a core–shell composite with a continuous carbon layer (C@M-Zn₂SnO₄ (H⁺)). The continuous structural evolution shortens Li⁺ diffusion paths, buffers mechanical stress, and stabilizes the sol-id-electrolyte interphase without altering the intrinisic lithium-storage mechanism of Zn₂SnO₄. As a result, the optimized C@M-Zn₂SnO₄ (H⁺) electrode delivers a reversible capacity of 650 mAh g⁻¹ after activation and retains 620 mAh g⁻¹ after 600 cycles at 200 mA g⁻¹, with Coulombic efficiency approaching 100% throughout. This work demon-strates that dimensionality-reduction-assisted structural engineering is an effective strategy for developing high-capacity, long-cycle-life anode materials.

Abstract: In the philosophy of language, Frege’s (1892) distinction between sense and reference provides a foundational framework for identity statements. Geach’s (1967) relative identity breaks out of the framework of absolute identity and opens another perspective for us. Putnam’s (1975) “Twin Earth” thought experiment, with its striking insight, pushes externalism to the extreme, successfully challenging the internalist model of meaning and setting the basic agenda for decades of subsequent debate on the problem of reference determination. However, despite the inspirational value of these groundbreaking works, a noteworthy phenomenon is that the debates they triggered—such as discussions around core cases like the Ship of Theseus and identical particles—seem to have reached a certain impasse. This paper argues that this impasse may not stem from the depth of the problems themselves, but precisely from a deep, unexamined presupposition shared by these otherwise highly persuasive theories: namely, that there exists a single, decisive category (whether microscopic physical structure or historical causality) capable of once and for all answering the question of identity. Instead of continuing to seek a better single answer under this presupposition, a more productive approach may be to reflect on the presupposition itself. To this end, we attempt to analyze the problem from a different angle. Interestingly, this angle shows that the aforementioned seemingly opposed excellent theories can actually all be understood as special cases of this theory under different categories; the difficulties they encounter become inevitable precisely when they attempt to make assertions across categories. Therefore, this paper is not intended to negate previous work, but to clarify the valid scope of its application, thereby providing a new path to resolve a series of philosophical difficulties arising from category mistakes.

Abstract: The El Duende ‘one-canvas’ model was developed as an arts-based practice for supervision in art therapy training. Responding to changes in institutional teaching structures, this case review reflects on its use in experiential training groups on one UK-based course, with the aim of developing understanding and theoretical insights that may inform future teaching practice. Eight training group facilitators retrospectively reviewed their experience of the model as applied in five experiential training groups over a three-month period. Data were analysed thematically through an iterative, collaborative, and reflexive process and four core themes were identified. Results are discussed with links made to Donald Winnicott’s ideas of creative destructiveness, use of the object, transitional space, and the holding environment. While limited in scope, results indicate that, through sustained cycles of repetition and return, the ‘one-canvas’ model served to hold intense transformational processes within a condensed timeframe, offering trainees a valuable experiential learning experience. The study builds on established research in the field, expanding previous applications of the model including theoretical understanding, and supporting innovation and reflection in art therapy education. Future research may consider further adaptations to the model, student perspectives, and its influence on personal and professional development.

Abstract: We address the paradoxical transformation of a classical-mechanical particle motion when the space and time scales of observation pass down the uncertainty principle threshold. This is analyzed in the language of classical statistical mechanics, considering specifically many-particle systems inhomogeneous along one spatial direction. We employ the density functional formalism in its square-gradient form and find: i) The macroscopic solution is analogous to the classical trajectory of a particle under a potential of force given by (minus) the free energy density. Whereas, ii) fluctuations around the solution in (i) are equal to the quantum-mechanical wave functions of a particle under a potential given by the curvature of the free energy density. We illustrate this situation with three textbook examples: A particle in a box, the harmonic oscillator, and the hydrogen atom. We show that their time-independent Schrödinger equation wave functions describe, respectively, the fluctuations of a fluid interface, of critical point fluctuations, and of a confined ideal gas. At large scales sharp probability distributions make fluctuations irrelevant, the vanishing of the first variation yields the macroscopically observable statistical-mechanical non-uniformity, equivalent to the classical particle trajectory. But at sufficiently small scales, with necessarily very few particles, distributions appear much wider, fluctuations dominate, and one obtains the Schrödinger equation (for the microscopic potential).

Abstract: Background: COVID-19 manifested with a wide range of clinical symptoms, including asymptomatic or mild illness and life-threatening respiratory failure. We hypothesized that elevated predictors during intensive care unit (ICU) stays would independently predict mortality and the subsequent development of post-COVID-19 condition (PCC). Methods: In this retrospective cohort study, we evaluated the clinical and laboratory determinants of mortality using hospital records in severe COVID-19 patients in conjunction with their PCC in survivors by self-reported survey. This research was conducted at XX University Hospital, and adult patients with laboratory-confirmed SARS-CoV-2 infection by RT-PCR assay who required ICU admission for respiratory failure or hemodynamic instability were included. Biomarkers were monitored throughout ICU stay to evaluate dynamic changes and their association with mortality. Survivors were followed from ICU discharge to assess readmissions, vaccination status, and post-COVID-19 condition, including fatigue, sleep disturbance, physical exhaustion, concentration problems, and memory impairment. Post-ICU mortality and hospitalization due to cardiovascular, respiratory, neurological, or other complications were recorded. Results: A total of 273 critically ill patients were included, of whom 112 survived and 161 died during ICU stay. Non-survivors were older and had lower mean arterial pressure. Intubation was more frequent among non-survivors, and APACHE II scores were significantly higher in this group. Hospitalizations due to cardiovascular, respiratory, and neurological complications were particularly associated with increased hazard of death (HRs ≥2, p-values < 0.05). Patients who died after ICU discharge (31.5%) had higher rates of fatigue, physical exhaustion, and memory impairment, suggesting a strong correlation between biomarker derangements during ICU stay and PCC. Conclusions: The present study was specifically designed to address this critical gap by evaluating whether biomarker trajectories during ICU follow-up not only predict in-hospital mortality but also serve as early determinants of Post-COVID status in survivors.

Abstract: The increasing accumulation of nano-/microplastics (NMPs) in agricultural soils has become an emerging environmental concern, posing risks to soil health, crop productivity, and food safety. Due to their persistence and small size, NMPs can disrupt soil structure, alter microbial communities, and facilitate the transport and uptake of contaminants by plants. In this context, biochar has attracted significant attention as a climate-smart soil amendment capable of improving soil quality while mitigating emerging pollutants. This review explores the potential role of biochar, including modified biochar, as a sustainable strategy for enhancing soil health and reducing the risks associated with NMPs contamination in agricultural systems. The unique physicochemical properties of biochar—such as its high surface area, porous structure, and abundant functional groups—enable interactions with plastic particles and associated contaminants through adsorption, aggregation, and immobilization processes. These interactions can reduce mobility, bioavailability, and plant uptake of NMPs in soil. In addition, biochar contributes to soil fertility improvement by enhancing nutrient retention, increasing water holding capacity, improving soil structure, and stimulating beneficial microbial activity. Biochar application also plays an important role in climate change mitigation by stabilizing carbon in soils and reducing greenhouse gas emissions from agricultural systems. Although biochar is considered a promising material for sustainability, some types of biochar may have adverse effects in saline–alkaline soils due to their high pH and salinity, particularly when produced at high pyrolysis temperatures. Overall, integrating biochar or modified biochar into sustainable agricultural practices offers multiple co-benefits, including soil restoration, pollutant mitigation, improved soil health, and enhanced climate resilience. This review synthesizes recent advances in understanding the mechanisms by which biochar influences NMPs behavior in soil–plant systems and highlights current knowledge gaps and future research directions needed to support its effective application in sustainable agriculture.

Abstract: Background/Objectives: The present study aimed to examine the relationship between Dark Triad personality traits (narcissism, psychopathy, and machiavellianism) and conspiratorial thinking. Additionally, it sought to investigate whether perceived social support acts as a mediator in this relationship, potentially serving as a protective factor against the adoption of conspiracy beliefs. Methods: The sample consisted of 620 participants (N = 620), including 523 women and 97 men, aged 18 to 69 (M = 35.74; SD = 11.36). Data were collected through an online survey using the Generic Conspiracist Beliefs Scale (GCBS), the Dirty Dozen Scale, and the Multidimensional Scale of Perceived Social Support (MSPSS). Results: Statistical analyses using Pearson’s correlation coefficient did not indicate a statistically significant co-occurrence between conspiratorial thinking and Dark Triad personality traits. Furthermore, the mediation models did not show significant values for mediating effects, suggesting that perceived social support—including its dimensions of support from a significant person, family, and friends—did not alter the relationship between personality traits and conspiracy thinking in this sample. Conclusions: The findings contradict several earlier reports, contributing to the ongoing debate regarding the dispositional roots of conspiracy beliefs. The results suggest that conspiratorial ideation may not be rooted in stable aversive personality traits, but instead may be driven by specific neurocognitive processes such as uncertainty processing and threat reactivity, aligning with current brain-based models of belief evaluation. Future research should integrate neuroscientific perspectives with social psychology to develop more comprehensive models of conspiratorial ideation.

Abstract: For the first time, a race survey of Puccinia graminis f. sp. tritici (Pgt) population was conducted on Triticum durum in the Altai region of Western Siberia, Russia. A total of 34 single pustule isolates with different virulence phenotypes were identified on durum wheat (Triticum durum) in 2025 and compared with Pgt from bread wheat (Triticum aestivum). The UPGMA-based clustering separated Pgt isolates into two distinct groups, suggesting the host-driven differentiation that was further proven using tools of population genetics. The pathogen isolates from durum showed a wider range of virulence complexity, higher variability, and greater average singularity. Virulence frequencies of Pgt on T. durum and T. aestivum differed markedly for Sr6, Sr7b, Sr9e, Sr17+13 and several other genes, while Sr24 and Sr31 remained effective independently of the pathogen origin. Two races, PKCSF and NFMSF, were detected on both the hosts, indicating a shared pathogen gene pool between bread and durum wheat. Even assuming host-specific divergence of Pgt in the Altai region, there is a need in deployment of the same resistance genes into both T. aestivum and T. durum cultivars to prevent an outbreak of stem rust in an event of favorable conditions for inoculum exchange between crops.

Abstract: Shannon entropy and Kolmogorov complexity describe complementary facets of information. We revisit Q2 from 27 Open Problems in Kolmogorov Complexity: whether all linear information inequalities including non‑Shannon‑type ones admit $\mathcal{O}(1)$-precision analogues for prefix‑free Kolmogorov complexity. We answer in the affirmative via two independent arguments. First, a contradiction proof leverages the uncomputability of $K$ to show that genuine algorithmic dependencies underlying non‑Shannon‑type constraints cannot incur length‑dependent overheads. Second, a coding‑theoretic construction treats the copy lemma as a bounded‑overhead coding mechanism and couples prefix‑free coding (Kraft's inequality) with typicality (Shannon-McMillan-Breiman) to establish $\mathcal{O}(1)$ precision; we illustrate the method on the Zhang-Yeung (ZY98) inequality and extend to all known non‑Shannon‑type inequalities derived through a finite number of copy operations. These results clarify the structural bridge between Shannon‑type linear inequalities and their Kolmogorov counterparts, and formalize artificial independence as the algorithmic analogue of copying in entropy proofs. Collectively, they indicate that the apparent discrepancy between statistical and algorithmic information manifests only as constant‑order effects under prefix complexity, thereby resolving a fundamental question about the relationship between statistical and algorithmic information structure.

Abstract: We give a constructive high-dimensional escape sequence for the equation (∆ − x ·∇)u = u associated with the symmetric Ornstein–Uhlenbeck operator in Gaussian space. Let (a_i)_i≥1 be a positive square summable sequence and let B_n = {x ∈ Rⁿ : ∑ⁿ_i=1 a_i² x_i² < 1}. We construct functions u_n that are continuous on Rⁿ, smooth on both sides of ∂B_n, solve the positive spectral equation away from ∂B_n, and have finite Gaussian H¹ energy. The construction uses a single real harmonic polynomial, Re(x1 +ix2)^m_n with m_n = ⌊n^1/8⌋, multiplied by the finite-energy Tricomi branch of the separated radial Ornstein–Uhlenbeck equation and then extended into B_n by the weighted Dirichlet principle. The exterior energy has a lower bound of order (2π)^n/2n^−1/2(2m_n/e)^m_n, whereas the interior minimizing energy is bounded by (2π)^n/2n^CC_a^m_n. Hence the ratio of total Gaussian H¹ energy to the energy inside B_n tends to infinity. The proof is written with all non-standard notation defined explicitly, and two examples, including an ℓ¹-small sequence with ∑_i a_i < 1, are included as checks of the hypotheses.

Abstract: Antisense oligonucleotides (ASOs) are a class of nucleic acid therapeutics that modulate gene expression through diverse mechanisms. Since their initial demonstration in inhibiting viral genes, advances in medicinal chemistry, pharmacology, and delivery have enabled robust and durable target engagement across multiple tissues. Chemical modifications to the backbone, ribose, and nucleobases have improved nuclease resistance, binding affinity, and pharmacokinetics, while conjugation and delivery technologies have expanded tissue accessibility. Beyond classical RNase H–mediated RNA degradation, ASOs regulate gene expression via splicing modulation, microRNA inhibition, transcriptional activation, and translation modulation, supporting both gene silencing and upregulation strategies. Multiple ASO drugs are now approved, particularly for genetic diseases, with many more in clinical development. This review outlines the evolution of antisense technology, key chemical and delivery innovations, ASO pharmacokinetics and intracellular trafficking, the mechanisms underlying gene regulation, and current clinical applications and future opportunities.

Abstract: Periodontal disease (PD) affects a large proportion of adults and is increasingly associated with systemic inflammation and neurodegenerative risk. However, current therapies have limited efficacy in disrupting biofilms and modulating systemic responses. In this pilot study, we evaluated nanoparticles (NPs) of epigallocatechin-3-gallate-palmitate (EGCG-palmitate or EC16), a lipid-soluble derivative of epigallocatechin-3-gallate (EGCG), generated using Facilitated Self-Assembling Technology (FAST). FAST is a green nanotechnology that enables spontaneous formation of stable nanoparticles without surfactants or carrier materials. We hypothesized that EC16 NPs could inhibit periodontal pathogens and modulate neuroinflammatory responses. Antimicrobial activity was assessed in vitro, and potential therapeutic effects were evaluated in a ligature + pathogen-induced periodontitis mouse model. EC16 NPs inhibited the growth of Porphyromonas gingivalis. Oral administration of EC16 NPs (0.02% w/v) at a dose equivalent to 16-20 mg/kg significantly reduced Porphyromonas gingivalis abundance and decreased alveolar bone loss by approximately 50% compared with controls. Importantly, biodistribution analysis using Cy5-labeled EC16 NPs demonstrated detectable signals in mouse brain tissue following oral gavage, indicating EC16 NPs can cross the blood–brain barrier. In addition, EC16 NP treatment was associated with increased regulatory T cell (Treg) populations in cervical lymph nodes and reduced expression of inflammatory (IL-1β) and senescence-related markers (p16, p53) in brain tissue. This represents, to our knowledge, the first evidence that an orally administered EGCG derivative in nanoparticle form reaches the central nervous system and induces biological responses. These findings demonstrate that EC16 nanoparticles possess dual local and systemic activity and support further investigation of FAST-enabled nanoformulations as a novel therapeutic strategy for periodontal disease and inflammation-related brain conditions.

Abstract: Spiking neural networks (SNNs) are often called the third generation of neural models. They communicate with brief asynchronous pulses rather than continuous values, which suits event-driven sensors and low-power neuromorphic hardware. The mathematics behind them is split across neuroscience textbooks, machine learning papers, and stochastic process literature, and a researcher entering the area runs into a notation problem before anything else. The same neuron model is written one way in a textbook, another way in a machine learning paper, and a third way in the stochastic process literature. This tutorial collects what a graduate student or research engineer needs in order to start working with SNNs, in one place and with one notation. We start with the leaky integrate-and-fire (LIF) neuron, derived from a conductance-based picture of the membrane. Reset semantics, the spike response model, and the broader family that includes Hodgkin-Huxley and adaptive exponential models are discussed. Network equations are written out for feedforward and recurrent architectures. We cover the main neural coding schemes (rate, time-to-first-spike, rank-order, phase, burst, population) and discuss when each is appropriate. The neuromorphic datasets a beginner is likely to encounter, including N-MNIST, DVS-Gesture, CIFAR10-DVS, SHD, and SSC, are described together with the tensor formats event-based data takes. For learning, we derive pair-based spike-timing-dependent plasticity from exponential traces and develop the surrogate gradient framework, which has become the dominant tool for training deep SNNs by backpropagation through time. Reset semantics, the choice of surrogate function, and common pitfalls in BPTT are addressed in a way that maps onto code. Alternative training paradigms (e-prop, EventProp, SLAYER, three-factor rules, ANN-to-SNN conversion) are introduced briefly so the reader knows what else is available. A practical section walks through a first SNN training loop with framework-agnostic pseudocode and points to the main software libraries (snnTorch, SpikingJelly, Norse). Throughout the article, equations carry derivational status labels (exact, reduction, approximation, heuristic) so that the reader sees at a glance which steps are mathematical identities and which involve approximations. We do not cover hardware implementation, detailed point process theory, expressivity proofs, or open problems in SNN complexity; these belong in a more advanced treatment. The article is meant to be read linearly, and a suggested reading path closes it.

Abstract: Generative artificial intelligence (GenAI) is increasingly integrated into higher education, where it supports writing, feedback, problem solving, and research-related tasks while also raising concerns about cognitive offloading and learner dependence. This scoping review mapped the literature on the relationships among GenAI, cognitive offloading, and learner agency in higher education. Peer-reviewed English-language studies were reviewed to examine how learner agency has been conceptualized, how GenAI may both enhance and erode agency, which mechanisms link GenAI use to educational outcomes, and which pedagogical conditions shape these effects. The review shows that learner agency is conceptualized as a multidimensional construct involving self-regulation, reflective judgement, intentionality, and responsible action. Across the literature, GenAI operates through a dual-pathway structure: one pathway may enhance learner agency by strengthening self-regulated learning, self-efficacy, feedback literacy, and reflective engagement, whereas the other may erode learner agency through cognitive offloading, overreliance, dependence, uncritical uptake, and weakened judgement. Overall, the findings suggest that the educational value of GenAI depends less on the technology itself than on how it is pedagogically embedded, with augmentation-oriented and scaffolded use being more supportive of learner agency than replacement-oriented use.

Abstract: Purpose: To develop an accelerator neutron source suitable for boron neutron capture therapy – a new promising method for treating malignant tumors, and to develop dosimetry tools and methods. Methods: Research into the transport and acceleration of a beam of charged particles, development and manufacture of an accelerator neutron source, and study of the radiation generated. Results: A facility called VITA has been created, which includes a tandem electrostatic accelerator of an original design for producing a 2.3 MeV 10 mA proton beam, a lithium target for generating neutrons in the 7Li(p,n)7Be reaction, and a beam shaping assembly for forming a therapeutic neutron beam. Also, tools and methods for measuring the boron dose, -ray dose, and sum of the fast neutron dose and the nitrogen dose have been proposed and created. The conducted studies demonstrated the high efficiency of the VITA facility, the possibility of implementing the prompt -ray spectroscopy for boron imaging, the possibility of implementing lithium neutron capture therapy, which has advantages over BNCT, and also presented the results of the development of tools and methods for measuring the boron dose, -ray dose, and the sum of the fast neutron dose and the nitrogen dose. Conclusion: The authors strongly recommend using the prompt -ray spectroscopy in treatment, developing lithium neutron capture therapy, including in combination with BNCT, and note the high efficiency, reliability and compactness of the VITA facility.

Abstract: The article examines a crisis of academic integrity triggered by the emergence of a network of “predatory” (fake) websites mimicking the official Webometrics Ranking of World Universities (WUR). The author analyzes the origins of this phenomenon, linked to the temporary closure of the official portal and the migration of the Cybermetrics Lab’s data to new storage platforms. Three primary clone domains were identified that either falsify data or sell “position enhancement services” for a fee (up to €5,000 per month). The problem has escalated into an “information wildfire,” spreading across 29 countries. The highest number of cases involving the use of fraudulent data was recorded in Indonesia (50% of cases), Turkey, and Ukraine. The use of false rankings in official university reports and the media is classified as institutional fraud, misleading both prospective students and the state. It is emphasized that under new legislation (e.g., the 2026 Ukrainian Law “On Academic Integrity”), the publication of such data may lead to legal liability for university management. The author calls for the establishment of an international consortium of universities to support the official webometric audit and purge the digital space of fraudulent ranking systems, proposing a series of measures and a Comprehensive Program for Enhancing Integrity and Transparency in University Ranking Ecosystems.